AdaBoost, Clearly Explained

AdaBoost combines weak learners, typically stumps, to enhance classification accuracy. It assigns varying importance to each stump based on their performance, allowing some to influence the final decision more than others. The method iteratively adjusts sample weights, ensuring that errors from previous stumps guide the creation of subsequent ones, ultimately improving classification results.